{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standardization\n",
    "\n",
    "\n",
    "Standardization centers the variable at zero and standardizing the variance to 1. \n",
    "\n",
    "The procedure involves subtracting the mean of each observation and then dividing by the standard deviation:\n",
    "\n",
    "**z = (x - x_mean) /  std**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.datasets import fetch_california_housing\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# the scaler - for standardization\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556\n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842\n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260\n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945\n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the California House price data from Scikit-learn\n",
    "X, y = fetch_california_housing(return_X_y=True, as_frame=True)\n",
    "\n",
    "# Remove 2 variables:\n",
    "X.drop(labels=[\"Latitude\", \"Longitude\"], axis=1, inplace=True)\n",
    "\n",
    "# display top 5 rows\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((14448, 6), (6192, 6))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's separate the data into training and testing sets\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X,\n",
    "    y,\n",
    "    test_size=0.3,\n",
    "    random_state=0,\n",
    ")\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standardisation: with the StandardScaler from sklearn\n",
    "\n",
    "# set up the scaler\n",
    "scaler = StandardScaler().set_output(transform=\"pandas\")\n",
    "\n",
    "# fit the scaler to the train set, it will learn the parameters\n",
    "scaler.fit(X_train)\n",
    "\n",
    "# transform train and test sets\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.86666741e+00, 2.86187016e+01, 5.42340368e+00, 1.09477484e+00,\n",
       "       1.42515732e+03, 3.04051776e+00])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the scaler stores the mean of the features, learned from train set\n",
    "\n",
    "scaler.mean_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.89109236e+00, 1.25962585e+01, 2.28754018e+00, 4.52736275e-01,\n",
       "       1.14954037e+03, 6.86792905e+00])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the scaler stores the standard deviation of the features,\n",
    "# learned from train set\n",
    "\n",
    "scaler.scale_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.880013</td>\n",
       "      <td>28.687984</td>\n",
       "      <td>5.442057</td>\n",
       "      <td>1.101109</td>\n",
       "      <td>1426.222061</td>\n",
       "      <td>3.140976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.920007</td>\n",
       "      <td>12.560416</td>\n",
       "      <td>2.862733</td>\n",
       "      <td>0.519956</td>\n",
       "      <td>1091.567168</td>\n",
       "      <td>15.796292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.499900</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.465753</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.552150</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>4.414452</td>\n",
       "      <td>1.006494</td>\n",
       "      <td>796.000000</td>\n",
       "      <td>2.436452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.529600</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.227365</td>\n",
       "      <td>1.048741</td>\n",
       "      <td>1169.500000</td>\n",
       "      <td>2.825041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.768750</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>6.064257</td>\n",
       "      <td>1.098434</td>\n",
       "      <td>1727.250000</td>\n",
       "      <td>3.285501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.000100</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>141.909091</td>\n",
       "      <td>25.636364</td>\n",
       "      <td>16305.000000</td>\n",
       "      <td>1243.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MedInc     HouseAge     AveRooms    AveBedrms    Population  \\\n",
       "count  6192.000000  6192.000000  6192.000000  6192.000000   6192.000000   \n",
       "mean      3.880013    28.687984     5.442057     1.101109   1426.222061   \n",
       "std       1.920007    12.560416     2.862733     0.519956   1091.567168   \n",
       "min       0.499900     1.000000     1.465753     0.500000      8.000000   \n",
       "25%       2.552150    18.000000     4.414452     1.006494    796.000000   \n",
       "50%       3.529600    29.000000     5.227365     1.048741   1169.500000   \n",
       "75%       4.768750    37.000000     6.064257     1.098434   1727.250000   \n",
       "max      15.000100    52.000000   141.909091    25.636364  16305.000000   \n",
       "\n",
       "          AveOccup  \n",
       "count  6192.000000  \n",
       "mean      3.140976  \n",
       "std      15.796292  \n",
       "min       0.692308  \n",
       "25%       2.436452  \n",
       "50%       2.825041  \n",
       "75%       3.285501  \n",
       "max    1243.333333  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect the original statistical parameters\n",
    "\n",
    "X_test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.007057</td>\n",
       "      <td>0.005500</td>\n",
       "      <td>0.008154</td>\n",
       "      <td>0.013991</td>\n",
       "      <td>0.000926</td>\n",
       "      <td>0.014627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.015290</td>\n",
       "      <td>0.997154</td>\n",
       "      <td>1.251446</td>\n",
       "      <td>1.148474</td>\n",
       "      <td>0.949568</td>\n",
       "      <td>2.300008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.780329</td>\n",
       "      <td>-2.192612</td>\n",
       "      <td>-1.730090</td>\n",
       "      <td>-1.313734</td>\n",
       "      <td>-1.232803</td>\n",
       "      <td>-0.341909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.695110</td>\n",
       "      <td>-0.843004</td>\n",
       "      <td>-0.441064</td>\n",
       "      <td>-0.194995</td>\n",
       "      <td>-0.547312</td>\n",
       "      <td>-0.087955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.178240</td>\n",
       "      <td>0.030271</td>\n",
       "      <td>-0.085698</td>\n",
       "      <td>-0.101679</td>\n",
       "      <td>-0.222400</td>\n",
       "      <td>-0.031374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.477017</td>\n",
       "      <td>0.665380</td>\n",
       "      <td>0.280150</td>\n",
       "      <td>0.008082</td>\n",
       "      <td>0.262794</td>\n",
       "      <td>0.035671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.887302</td>\n",
       "      <td>1.856210</td>\n",
       "      <td>59.664826</td>\n",
       "      <td>54.207251</td>\n",
       "      <td>12.944167</td>\n",
       "      <td>180.591967</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MedInc     HouseAge     AveRooms    AveBedrms   Population  \\\n",
       "count  6192.000000  6192.000000  6192.000000  6192.000000  6192.000000   \n",
       "mean      0.007057     0.005500     0.008154     0.013991     0.000926   \n",
       "std       1.015290     0.997154     1.251446     1.148474     0.949568   \n",
       "min      -1.780329    -2.192612    -1.730090    -1.313734    -1.232803   \n",
       "25%      -0.695110    -0.843004    -0.441064    -0.194995    -0.547312   \n",
       "50%      -0.178240     0.030271    -0.085698    -0.101679    -0.222400   \n",
       "75%       0.477017     0.665380     0.280150     0.008082     0.262794   \n",
       "max       5.887302     1.856210    59.664826    54.207251    12.944167   \n",
       "\n",
       "          AveOccup  \n",
       "count  6192.000000  \n",
       "mean      0.014627  \n",
       "std       2.300008  \n",
       "min      -0.341909  \n",
       "25%      -0.087955  \n",
       "50%      -0.031374  \n",
       "75%       0.035671  \n",
       "max     180.591967  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect the statistics after scaling\n",
    "\n",
    "X_test_scaled.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.444800e+04</td>\n",
       "      <td>1.444800e+04</td>\n",
       "      <td>1.444800e+04</td>\n",
       "      <td>1.444800e+04</td>\n",
       "      <td>1.444800e+04</td>\n",
       "      <td>1.444800e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.999815e-17</td>\n",
       "      <td>-1.077027e-16</td>\n",
       "      <td>-4.873670e-16</td>\n",
       "      <td>2.564701e-16</td>\n",
       "      <td>-6.393311e-17</td>\n",
       "      <td>5.606442e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000035e+00</td>\n",
       "      <td>1.000035e+00</td>\n",
       "      <td>1.000035e+00</td>\n",
       "      <td>1.000035e+00</td>\n",
       "      <td>1.000035e+00</td>\n",
       "      <td>1.000035e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.780329e+00</td>\n",
       "      <td>-2.192612e+00</td>\n",
       "      <td>-2.000948e+00</td>\n",
       "      <td>-1.681865e+00</td>\n",
       "      <td>-1.237153e+00</td>\n",
       "      <td>-3.335092e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-6.853538e-01</td>\n",
       "      <td>-8.430044e-01</td>\n",
       "      <td>-4.245411e-01</td>\n",
       "      <td>-1.963688e-01</td>\n",
       "      <td>-5.577510e-01</td>\n",
       "      <td>-8.929172e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-1.732160e-01</td>\n",
       "      <td>3.027077e-02</td>\n",
       "      <td>-8.484487e-02</td>\n",
       "      <td>-1.015919e-01</td>\n",
       "      <td>-2.263142e-01</td>\n",
       "      <td>-3.263905e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.597515e-01</td>\n",
       "      <td>6.653800e-01</td>\n",
       "      <td>2.728762e-01</td>\n",
       "      <td>1.154128e-02</td>\n",
       "      <td>2.599671e-01</td>\n",
       "      <td>3.493450e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.887302e+00</td>\n",
       "      <td>1.856210e+00</td>\n",
       "      <td>5.556621e+01</td>\n",
       "      <td>7.282803e+01</td>\n",
       "      <td>2.980047e+01</td>\n",
       "      <td>8.687827e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             MedInc      HouseAge      AveRooms     AveBedrms    Population  \\\n",
       "count  1.444800e+04  1.444800e+04  1.444800e+04  1.444800e+04  1.444800e+04   \n",
       "mean   8.999815e-17 -1.077027e-16 -4.873670e-16  2.564701e-16 -6.393311e-17   \n",
       "std    1.000035e+00  1.000035e+00  1.000035e+00  1.000035e+00  1.000035e+00   \n",
       "min   -1.780329e+00 -2.192612e+00 -2.000948e+00 -1.681865e+00 -1.237153e+00   \n",
       "25%   -6.853538e-01 -8.430044e-01 -4.245411e-01 -1.963688e-01 -5.577510e-01   \n",
       "50%   -1.732160e-01  3.027077e-02 -8.484487e-02 -1.015919e-01 -2.263142e-01   \n",
       "75%    4.597515e-01  6.653800e-01  2.728762e-01  1.154128e-02  2.599671e-01   \n",
       "max    5.887302e+00  1.856210e+00  5.556621e+01  7.282803e+01  2.980047e+01   \n",
       "\n",
       "           AveOccup  \n",
       "count  1.444800e+04  \n",
       "mean   5.606442e-17  \n",
       "std    1.000035e+00  \n",
       "min   -3.335092e-01  \n",
       "25%   -8.929172e-02  \n",
       "50%   -3.263905e-02  \n",
       "75%    3.493450e-02  \n",
       "max    8.687827e+01  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams.update({\"font.size\": 15})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_test.hist(bins=20, figsize=(20, 12), layout=(2, 3))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_test_scaled.hist(bins=20, figsize=(20, 12), layout=(2, 3))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fsml",
   "language": "python",
   "name": "fsml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
